1 Loading of data

1.1 Data set

First, we load, filter, and merge the data sets.

How does the data set looks like

1.2 Set tresholds

Applied thresholds are indicated by grey horizontal line.

1.2.1 Mean_Puncta_mitoTracker_AreaShape_Area

1.2.2 Mean_Puncta_mitoTracker_Number_Object_Number

1.2.3 mitoTracker_MeanArea

1.2.4 mitoTracker_MeanCount

1.2.5 mitoTracker_MeanLength

1.2.6 Branchpoints

1.3 Counts per sample

#Apply tresholds
data <- subset(data, Mean_Puncta_mitoTracker_AreaShape_Area < 200)
data <- subset(data, Mean_Puncta_mitoTracker_Number_Object_Number < 1400)
data <- subset(data, mitoTracker_MeanArea < 0.04)
data <- subset(data, mitoTracker_MeanCount < 0.35)
data <- subset(data, mitoTracker_MeanLength < 0.07)
data <- subset(data, Branchpoints < 100)

#Save data set
write.csv(data, file = "results_mitoTracker/tables/data_mitoTracker.csv")

Cell counts per cell line:

#data <- read.csv("results_mitoTracker/tables/data_mitoTracker.csv")
table(data$Metadata_SampleID)
## 
## i1JF-R1-018 iG3G-R1-039 i1E4-R1-003 iO3H-R1-005 i82A-R1-002 iJ2C-R1-015 
##         626         108         482           0         393         529 
## iM89-R1-005 iC99-R1-007 iR66-R1-007 iAY6-R1-003 iPX7-R1-001 i88H-R1-002 
##          55         292         155         314         449         519

Mean cell count:

mean(table(data$Metadata_SampleID))
## [1] 326.8333

2 Visualize mitochondrial parameters

Various mitochondrial parameters are visualized for each patient-derived cell line as well as for the disease state Mean Ctrl levels are indicated by grey horizontal line.

2.1 MitoTracker Area

2.1.1 each sample

2.1.2 disease-state

2.2 MitoTracker Count

2.2.1 each sample

2.2.2 disease-state

2.3 MitoTracker Intensity

2.3.1 each sample

2.3.2 disease-state

2.4 MitoTracker Mean Area

2.4.1 each sample

2.4.2 disease-state

2.5 MitoTracker Mean Count

2.5.1 each sample

2.5.2 disease-state

2.6 MitoTracker Number Branch Ends

2.6.1 each sample

2.6.2 disease-state

2.7 MitoTracker Number Branchpoints

2.7.1 each sample

2.7.2 disease-state

2.8 MitoTracker Skeleton Length

2.8.1 each sample

2.8.2 disease-state

2.9 MitoTracker Mean Skeleton Length

2.9.1 each sample

2.9.2 disease-state

3 Statistical testing using linear mixed effects models

Nested approach (“Mitochondrial Parameter” ~ Disease_state + (1 | Disease_state:Metadata_SampleID)) to compensate for dependencies within the groups.

3.1 MitoTracker Area

## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: Mean_Puncta_mitoTracker_AreaShape_Area ~ Disease_state + (1 |  
##     Disease_state:Metadata_SampleID)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  37655.7  37680.8 -18823.8  37647.7     3918 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.3135 -0.7153 -0.2849  0.3660  5.2543 
## 
## Random effects:
##  Groups                          Name        Variance Std.Dev.
##  Disease_state:Metadata_SampleID (Intercept)  13.77    3.71   
##  Residual                                    859.40   29.32   
## Number of obs: 3922, groups:  Disease_state:Metadata_SampleID, 11
## 
## Fixed effects:
##                  Estimate Std. Error t value
## (Intercept)       41.1424     2.0452  20.116
## Disease_statesPD  -0.2975     2.5854  -0.115
## 
## Correlation of Fixed Effects:
##             (Intr)
## Diss_sttsPD -0.791
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: Mean_Puncta_mitoTracker_AreaShape_Area
##                Chisq Df Pr(>Chisq)
## Disease_state 0.0132  1     0.9084

3.2 MitoTracker Count

## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: Mean_Puncta_mitoTracker_Number_Object_Number ~ Disease_state +  
##     (1 | Disease_state:Metadata_SampleID)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  54356.5  54381.6 -27174.2  54348.5     3918 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.9826 -0.7879 -0.1063  0.6683  3.8460 
## 
## Random effects:
##  Groups                          Name        Variance Std.Dev.
##  Disease_state:Metadata_SampleID (Intercept)  3999     63.24  
##  Residual                                    60535    246.04  
## Number of obs: 3922, groups:  Disease_state:Metadata_SampleID, 11
## 
## Fixed effects:
##                  Estimate Std. Error t value
## (Intercept)        396.76      32.50  12.208
## Disease_statesPD    41.62      40.87   1.018
## 
## Correlation of Fixed Effects:
##             (Intr)
## Diss_sttsPD -0.795
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: Mean_Puncta_mitoTracker_Number_Object_Number
##                Chisq Df Pr(>Chisq)
## Disease_state 1.0371  1     0.3085

3.3 MitoTracker Intensity

## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: Mean_Puncta_mitoTracker_Intensity_MeanIntensity_Corr_mitoTracker ~  
##     Disease_state + (1 | Disease_state:Metadata_SampleID)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  -9970.9  -9945.8   4989.4  -9978.9     3918 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9958 -0.5368 -0.0929  0.4477  6.5618 
## 
## Random effects:
##  Groups                          Name        Variance Std.Dev.
##  Disease_state:Metadata_SampleID (Intercept) 0.003624 0.06020 
##  Residual                                    0.004528 0.06729 
## Number of obs: 3922, groups:  Disease_state:Metadata_SampleID, 11
## 
## Fixed effects:
##                  Estimate Std. Error t value
## (Intercept)       0.16013    0.03017   5.307
## Disease_statesPD  0.02586    0.03783   0.683
## 
## Correlation of Fixed Effects:
##             (Intr)
## Diss_sttsPD -0.797
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: Mean_Puncta_mitoTracker_Intensity_MeanIntensity_Corr_mitoTracker
##               Chisq Df Pr(>Chisq)
## Disease_state 0.467  1     0.4944

3.4 MitoTracker Mean Area

## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: 
## mitoTracker_MeanArea ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
## -26721.3 -26696.2  13364.7 -26729.3     3918 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.2166 -0.6724 -0.3272  0.3302  4.0370 
## 
## Random effects:
##  Groups                          Name        Variance  Std.Dev.
##  Disease_state:Metadata_SampleID (Intercept) 2.921e-06 0.001709
##  Residual                                    6.374e-05 0.007984
## Number of obs: 3922, groups:  Disease_state:Metadata_SampleID, 11
## 
## Fixed effects:
##                    Estimate Std. Error t value
## (Intercept)       0.0091144  0.0008883  10.261
## Disease_statesPD -0.0013920  0.0011182  -1.245
## 
## Correlation of Fixed Effects:
##             (Intr)
## Diss_sttsPD -0.794
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: mitoTracker_MeanArea
##                Chisq Df Pr(>Chisq)
## Disease_state 1.5496  1     0.2132

3.5 MitoTracker Mean Count

## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: 
## mitoTracker_MeanCount ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  -9545.0  -9519.9   4776.5  -9553.0     3918 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.5974 -0.7433 -0.2424  0.4793  3.6918 
## 
## Random effects:
##  Groups                          Name        Variance  Std.Dev.
##  Disease_state:Metadata_SampleID (Intercept) 0.0005318 0.02306 
##  Residual                                    0.0050756 0.07124 
## Number of obs: 3922, groups:  Disease_state:Metadata_SampleID, 11
## 
## Fixed effects:
##                   Estimate Std. Error t value
## (Intercept)       0.085316   0.011736   7.269
## Disease_statesPD -0.004699   0.014744  -0.319
## 
## Correlation of Fixed Effects:
##             (Intr)
## Diss_sttsPD -0.796
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: mitoTracker_MeanCount
##                Chisq Df Pr(>Chisq)
## Disease_state 0.1016  1     0.7499

3.6 MitoTracker Number Branch Ends

## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: 
## ObjectSkeleton_NumberBranchEnds_mitoTracker_Skeleton ~ Disease_state +  
##     (1 | Disease_state:Metadata_SampleID)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  25258.8  25283.9 -12625.4  25250.8     3918 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2547 -0.6947 -0.3338  0.3346  5.7230 
## 
## Random effects:
##  Groups                          Name        Variance Std.Dev.
##  Disease_state:Metadata_SampleID (Intercept)  6.777   2.603   
##  Residual                                    36.201   6.017   
## Number of obs: 3922, groups:  Disease_state:Metadata_SampleID, 11
## 
## Fixed effects:
##                  Estimate Std. Error t value
## (Intercept)         4.880      1.315   3.711
## Disease_statesPD    1.411      1.650   0.855
## 
## Correlation of Fixed Effects:
##             (Intr)
## Diss_sttsPD -0.797
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: ObjectSkeleton_NumberBranchEnds_mitoTracker_Skeleton
##                Chisq Df Pr(>Chisq)
## Disease_state 0.7309  1     0.3926

3.7 MitoTracker Number Branchpoints

## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: Branchpoints ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  34891.7  34916.8 -17441.8  34883.7     3918 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.8271 -0.6447 -0.4118  0.3099  4.1707 
## 
## Random effects:
##  Groups                          Name        Variance Std.Dev.
##  Disease_state:Metadata_SampleID (Intercept)  47.16    6.867  
##  Residual                                    422.68   20.559  
## Number of obs: 3922, groups:  Disease_state:Metadata_SampleID, 11
## 
## Fixed effects:
##                  Estimate Std. Error t value
## (Intercept)        15.286      3.491   4.378
## Disease_statesPD    3.866      4.386   0.882
## 
## Correlation of Fixed Effects:
##             (Intr)
## Diss_sttsPD -0.796
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: Branchpoints
##                Chisq Df Pr(>Chisq)
## Disease_state 0.7772  1      0.378

3.8 MitoTracker Skeleton Length

## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: ObjectSkeleton_TotalObjectSkeletonLength_mitoTracker_Skeleton ~  
##     Disease_state + (1 | Disease_state:Metadata_SampleID)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
##  45382.3  45407.4 -22687.1  45374.3     3918 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.6787 -0.5941 -0.3749  0.2426  5.2837 
## 
## Random effects:
##  Groups                          Name        Variance Std.Dev.
##  Disease_state:Metadata_SampleID (Intercept) 2025     45.0    
##  Residual                                    6115     78.2    
## Number of obs: 3922, groups:  Disease_state:Metadata_SampleID, 11
## 
## Fixed effects:
##                  Estimate Std. Error t value
## (Intercept)         52.20      22.63   2.307
## Disease_statesPD    25.71      28.39   0.906
## 
## Correlation of Fixed Effects:
##             (Intr)
## Diss_sttsPD -0.797
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: ObjectSkeleton_TotalObjectSkeletonLength_mitoTracker_Skeleton
##                Chisq Df Pr(>Chisq)
## Disease_state 0.8202  1     0.3651

3.9 MitoTracker Mean Skeleton Length

## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: 
## mitoTracker_MeanLength ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
##    Data: data
## 
##      AIC      BIC   logLik deviance df.resid 
## -22949.9 -22924.8  11479.0 -22957.9     3918 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.2652 -0.6325 -0.4190  0.2908  4.7185 
## 
## Random effects:
##  Groups                          Name        Variance  Std.Dev.
##  Disease_state:Metadata_SampleID (Intercept) 7.820e-06 0.002796
##  Residual                                    1.667e-04 0.012913
## Number of obs: 3922, groups:  Disease_state:Metadata_SampleID, 11
## 
## Fixed effects:
##                  Estimate Std. Error t value
## (Intercept)      0.008381   0.001452   5.772
## Disease_statesPD 0.001726   0.001828   0.944
## 
## Correlation of Fixed Effects:
##             (Intr)
## Diss_sttsPD -0.794
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: mitoTracker_MeanLength
##                Chisq Df Pr(>Chisq)
## Disease_state 0.8919  1      0.345